federated-learning-with-differential-privacy's Introduction

Federated Learning

This is an implementation of Federated Learning (FL) with Differential Privacy (DP). The FL algorithm is FedAvg, based on the paper Communication-Efficient Learning of Deep Networks from Decentralized Data. Each client trains local model by DP-SGD [2] to perturb model parameters. The noise multiplier is determined by [3-5] (see rdp_analysis.py).

Requirements

torch, torchvision
numpy
scipy

Files

FLModel.py: definition of the FL client and FL server class

MLModel.py: CNN model for MNIST datasets

rdp_analysis.py: RDP for subsampled Gaussian [3], convert RDP to DP by Ref. [4, 5] (tighter privacy analysis than [2]).

utils.py: sample MNIST in a non-i.i.d. manner

Usag

Run test_cnn.ipynb

FL model parameters

# code segment in test_cnn.ipynb
lr = 0.1
fl_param = {
    'output_size': 10,          # number of units in output layer
    'client_num': client_num,   # number of clients
    'model': MNIST_CNN,  # model
    'data': d,           # dataset
    'lr': lr,            # learning rate
    'E': 500,            # number of local iterations
    'eps': 4.0,          # privacy budget
    'delta': 1e-5,       # approximate differential privacy: (epsilon, delta)-DP
    'q': 0.01,           # sampling rate
    'clip': 0.2,         # clipping norm
    'tot_T': 10,         # number of aggregation times (communication rounds)
}

References

[1] McMahan, Brendan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-Efficient Learning of Deep Networks from Decentralized Data. In AISTATS, 2017.

[2] Abadi, Martin, et al. Deep learning with differential privacy. In CCS. 2016.

[3] Mironov, Ilya, Kunal Talwar, and Li Zhang. R'enyi differential privacy of the sampled gaussian mechanism. arXiv preprint 2019.

[4] Canonne, Clément L., Gautam Kamath, and Thomas Steinke. The discrete gaussian for differential privacy. In NeurIPS, 2020.

[5] Asoodeh, S., Liao, J., Calmon, F.P., Kosut, O. and Sankar, L., A better bound gives a hundred rounds: Enhanced privacy guarantees via f-divergences. In ISIT, 2020.

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